Teaching
Assistant

Course
Description

cs440 is an
introductory
course about
the field known as Artificial Intelligence. Artificial Intelligence
is not only about building an “artificial mind”, a
robot or the
world’s best chess playing program. We will learn how to
mathematically model (simple) tasks in real world that deal with
perception and action: how a computer may use video cameras and
gyroscopes to autonomously fly a helicopter, how to design algorithms
that search through the web looking for pieces of information that
exactly answer our questions, and how to understand those questions
in the first place. To do this we will need to know how to
conveniently (from computational perspective) represent our knowledge
about those tasks, how to infer best actions (motor torque) based on
the perceived state of the world (helicopter’s camera seems
to
“see” a tree in front of the helicopter), and how
to
systematically learn further facts about those tasks from the
knowledge and percepts.

Course
Policies and Procedures

Course Web Site We
will be
using
Rutgers' E-companion online course site, www.rutgersonline.net. Each
registered student should have received access information for
RutgersOnline. All notices and assignments will be posted on
RutgersOnline. Homework assignments are to be turned in through
RutgersOnline Dropboxes. Please get familiar with the site. Let me
know if you have any problems accessing the site.

Homework

Homework will be assigned approx. every
two weeks. You should work on the assignment independently. After
all, homework is there to help you learn and understand the topics we
cover.
Homework will include “theoretical”
and “programming exercises. We will use Lisp and LUSH
(http://lush.sf.net) as programming languages / environments.
At the time homework is assigned you
will be given the due date. All homework is to be turned in by
11:59pm on the due date through RutgersOnline Dropboxes. Late
homework is accepted with the following penalty:

Hours Late

Penalty

(0,24]

-20%

(24,48]

-50%

(48,∞)

-100%

Therefore, if you
turned in
your
assignment at 2:10am the day after the due date and you solved all
problems correctly, you will get a score of 80 instead of 100. See
Grading Policy for further details.

Tests

There will be two
tests in
the course
of this class: a midterm test (late-october) and a final test. Both
tests are closed books, no notes or cheatsheets.

Coursework

All your work in the
course
should be
governed by the Rutgers and CS Dept. Policies on Academic Integrity. This, among
other things, means that no dishonesty, no cheating on tests, etc. is
allowed. Please respect your fellow students' work!

Grading
Policy

Grading
Criteria

You final grade will
be
based on how
well you perform on homeworks, midterm and final exams. Each
performance item will weigh according to the table below.

Item

Weight

Homework

30%

Midterm

30%

Final

40%

Grading
Scale

Each time you turn in
an
assignment (or
a test) you will be given a numeric score S between 0 (worst) and 100
(best). The score will then be converted to a normalized score:

Sn = 3/2 ( S - E[S] )
/
StdDev[S] + 4

truncated to the
range
[0,7]. This, for
instance, means that if you score 60 points on the final, the class
average is 50 and the standard deviation is 30, your normalized score
will be 4.5. The normalized score will correspond to the letter
grades in the table below. Hence, in the example above 4.5 would
correspond to letter grade B.

Normalized Score

Grade

[6.0,7.0]

A

[5.0,6.0)

B+

[4.0,5.0)

B

[3.0,4.0)

C+

[2.0,3.0)

C

[1.0,2.0)

D

[0,1.0)

F

Final course grade
will be
assigned
based on the weighted average score of all assignments and tests:

FSn = 0.3 [ Sn(HW1) +
...
Sn(HWN) ] / N
+ 0.3 Sn(Mid) + 0.4 Sn(Fnl).

After computing the
final
score I may
adjust it based on my overall impression of your performance as well
as make adjustments in case of multimodal score distributions. Your
final course letter grade will be computed from the final numeric
score FSn and the table above.